Placeholder Image

Subtitles section Play video

  • This is a footnote to the main video about mathematically protecting the privacy of individuals

  • when youre publishing statistics from a private dataset, like a medical study, or

  • a census, or whatever.

  • There are in fact two kinds of privacy violation that can happen from a survey, and theyre

  • qualitatively very different from each other.

  • The first kind is the direct breach of the privacy of an individual by somehow revealing

  • private information specific to them (like their birthday, or blood type, or Harry Potter

  • house), and this is the kind of privacy violation the main video focused on.

  • The second kind is an indirect violation of privacy via association with a group (like

  • how men are more likely to be overpaid, or how Slytherins are more likely to be evil,

  • or how overpaid men are more likely to be Slytherins…).

  • Of course, revealing trend-based information about a group is precisely the purpose of

  • doing surveys; we, as a society, want to know the expected lifespan of smokers vs non-smokers,

  • or the typical month in which professional hockey players are born.

  • But if a survey reveals that hockey players are more likely to have January birthdays,

  • then knowing somebody plays in the NHL gives you insight into a supposedly private piece

  • of information, and does so regardless of whether or not that player themselves participated

  • in the survey!

  • If we wanted to protect the privacy of individuals 100%, pretty much the only option would be

  • to outright prohibit all studies and surveys that use any individual information, whatsoever.

  • But then we couldn’t have representative democracies, or study diseases, or keep an

  • eye out for dark wizards coming out of Slytherin, or lots of other useful things.

  • So if you are going to do a study, the best you can do is to not violate any participant’s

  • privacy more than their privacy would have been violated if they hadn’t participated

  • in the study.

  • That is, the current wisdom is that it’s ok to reveal that NHL players are more likely

  • to have January birthdays , but it’s not ok to reveal the birthday of a specific player.

  • And of course, you’d have to reveal the January birthdays fact using a mathematically

  • guaranteed privacy protectionbut that’s what the main video is about.

  • And if you want to ensure you don’t have online information specific to you stolen

  • or published, I highly recommend using Dashlane, this video’s sponsor and a service/tool

  • that can greatly simplify and secure your online life (as it has mine) - with Dashlane,

  • every single site or online service you use gets a strong, unique password, and Dashlane

  • securely remembers them so you don’t have to.

  • Dashlane also (with your permission) auto-fills online address forms, credit card info, saving

  • you time and hassle.

  • It’s really nice.

  • You can get a free 30 day trial of dashlane premium (which also includes Dashlane’s

  • VPN) by going to dashlane.com/minutephysics.

  • Again, that’s dashlane.com/minutephysics, and use coupon code minutephysics for 10%

  • off at checkout.

  • And I’d like to thank Dashlane for simplifying and securing my life.

This is a footnote to the main video about mathematically protecting the privacy of individuals

Subtitles and vocabulary

Operation of videos Adjust the video here to display the subtitles

B1 dashlane privacy violation slytherins survey nhl

When It's OK to Violate Privacy

  • 32 1
    林宜悉 posted on 2020/03/28
Video vocabulary